Volunteer Summary - MMH

Table

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sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: sets ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.31) and the part related to the fixed effects alone (marginal R2) is of 0.18. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 21.79 (S.E. = 2.31, p < .001, 95% CI [17.27, 26.31]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -0.03, S.E. = 0.03, p = 0.274, 95% CI [-0.09, 0.03]; Std. beta = -0.18, 95% CI [-0.51, 0.14])
• The effect of gender [male] is statistically non-significant and positive (beta = 0.66, S.E. = 0.99, p = 0.505, 95% CI [-1.29, 2.61]; Std. beta = 0.28, 95% CI [-0.55, 1.11])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 0.69, S.E. = 0.78, p = 0.373, 95% CI [-0.83, 2.22]; Std. beta = 0.29, 95% CI [-0.35, 0.94])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.62, S.E. = 0.49, p = 0.201, 95% CI [-0.33, 1.58]; Std. beta = 0.23, 95% CI [-0.12, 0.59])
• The effect of attendance is statistically non-significant and negative (beta = -0.36, S.E. = 0.39, p = 0.359, 95% CI [-1.13, 0.41]; Std. beta = -0.15, 95% CI [-0.48, 0.17])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.52, S.E. = 0.80, p = 0.515, 95% CI [-1.04, 2.08]; Std. beta = 0.22, 95% CI [-0.44, 0.88])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: setv ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.16. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 13.62 (S.E. = 2.09, p < .001, 95% CI [9.53, 17.71]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -0.01, S.E. = 0.03, p = 0.687, 95% CI [-0.06, 0.04]; Std. beta = -0.08, 95% CI [-0.45, 0.30])
• The effect of gender [male] is statistically non-significant and positive (beta = 0.77, S.E. = 0.91, p = 0.397, 95% CI [-1.01, 2.54]; Std. beta = 0.41, 95% CI [-0.53, 1.35])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and negative (beta = -0.54, S.E. = 0.70, p = 0.438, 95% CI [-1.92, 0.83]; Std. beta = -0.29, 95% CI [-1.02, 0.44])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.51, S.E. = 0.33, p = 0.127, 95% CI [-0.14, 1.15]; Std. beta = 0.24, 95% CI [-0.07, 0.54])
• The effect of attendance is statistically non-significant and negative (beta = -0.49, S.E. = 0.36, p = 0.167, 95% CI [-1.19, 0.21]; Std. beta = -0.26, 95% CI [-0.63, 0.11])
• The effect of time point [2nd] is statistically significant and positive (beta = 1.01, S.E. = 0.51, p = 0.047, 95% CI [0.01, 2.00]; Std. beta = 0.53, 95% CI [6.53e-03, 1.06])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: maks ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.77) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 45.31 (S.E. = 5.06, p < .001, 95% CI [35.40, 55.23]). Within this model:

• The effect of age is statistically non-significant and positive (beta = 0.05, S.E. = 0.07, p = 0.422, 95% CI [-0.08, 0.18]; Std. beta = 0.18, 95% CI [-0.26, 0.62])
• The effect of gender [male] is statistically non-significant and positive (beta = 2.26, S.E. = 2.20, p = 0.305, 95% CI [-2.06, 6.58]; Std. beta = 0.58, 95% CI [-0.53, 1.70])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and negative (beta = -1.37, S.E. = 1.69, p = 0.420, 95% CI [-4.68, 1.95]; Std. beta = -0.35, 95% CI [-1.21, 0.50])
• The effect of number volunteer r is statistically non-significant and negative (beta = -0.40, S.E. = 0.57, p = 0.487, 95% CI [-1.52, 0.72]; Std. beta = -0.09, 95% CI [-0.35, 0.17])
• The effect of attendance is statistically non-significant and negative (beta = -0.49, S.E. = 0.87, p = 0.575, 95% CI [-2.19, 1.22]; Std. beta = -0.13, 95% CI [-0.57, 0.31])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.37, S.E. = 0.85, p = 0.667, 95% CI [-1.30, 2.03]; Std. beta = 0.09, 95% CI [-0.34, 0.53])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: ibs ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.65) and the part related to the fixed effects alone (marginal R2) is of 0.30. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 17.54 (S.E. = 1.84, p < .001, 95% CI [13.93, 21.14]). Within this model:

• The effect of age is statistically non-significant and positive (beta = 9.93e-03, S.E. = 0.02, p = 0.675, 95% CI [-0.04, 0.06]; Std. beta = 0.07, 95% CI [-0.27, 0.42])
• The effect of gender [male] is statistically significant and negative (beta = -1.62, S.E. = 0.80, p = 0.042, 95% CI [-3.18, -0.06]; Std. beta = -0.90, 95% CI [-1.76, -0.03])
• The effect of previous volunteer [yes_within_12_month] is statistically significant and negative (beta = -1.23, S.E. = 0.62, p = 0.046, 95% CI [-2.44, -0.02]; Std. beta = -0.68, 95% CI [-1.35, -0.01])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.12, S.E. = 0.30, p = 0.677, 95% CI [-0.46, 0.71]; Std. beta = 0.06, 95% CI [-0.23, 0.35])
• The effect of attendance is statistically non-significant and negative (beta = -0.37, S.E. = 0.31, p = 0.242, 95% CI [-0.98, 0.25]; Std. beta = -0.20, 95% CI [-0.54, 0.14])
• The effect of time point [2nd] is statistically significant and positive (beta = 1.04, S.E. = 0.46, p = 0.023, 95% CI [0.14, 1.94]; Std. beta = 0.58, 95% CI [0.08, 1.08])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: ers_e ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.70) and the part related to the fixed effects alone (marginal R2) is of 0.10. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 12.96 (S.E. = 1.43, p < .001, 95% CI [10.16, 15.75]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -3.04e-03, S.E. = 0.02, p = 0.869, 95% CI [-0.04, 0.03]; Std. beta = -0.04, 95% CI [-0.47, 0.39])
• The effect of gender [male] is statistically non-significant and negative (beta = -0.63, S.E. = 0.62, p = 0.310, 95% CI [-1.85, 0.59]; Std. beta = -0.56, 95% CI [-1.65, 0.52])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and negative (beta = -0.04, S.E. = 0.48, p = 0.933, 95% CI [-0.98, 0.90]; Std. beta = -0.04, 95% CI [-0.87, 0.80])
• The effect of number volunteer r is statistically significant and positive (beta = 0.43, S.E. = 0.19, p = 0.020, 95% CI [0.07, 0.80]; Std. beta = 0.34, 95% CI [0.05, 0.63])
• The effect of attendance is statistically non-significant and negative (beta = -0.15, S.E. = 0.25, p = 0.535, 95% CI [-0.63, 0.33]; Std. beta = -0.14, 95% CI [-0.56, 0.29])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.50, S.E. = 0.28, p = 0.076, 95% CI [-0.05, 1.05]; Std. beta = 0.44, 95% CI [-0.05, 0.93])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: ers_r ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.48) and the part related to the fixed effects alone (marginal R2) is of 0.12. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 13.18 (S.E. = 1.61, p < .001, 95% CI [10.02, 16.33]). Within this model:

• The effect of age is statistically non-significant and positive (beta = 6.87e-03, S.E. = 0.02, p = 0.739, 95% CI [-0.03, 0.05]; Std. beta = 0.06, 95% CI [-0.31, 0.44])
• The effect of gender [male] is statistically non-significant and negative (beta = -0.84, S.E. = 0.70, p = 0.229, 95% CI [-2.21, 0.53]; Std. beta = -0.58, 95% CI [-1.53, 0.37])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 0.11, S.E. = 0.54, p = 0.832, 95% CI [-0.95, 1.18]; Std. beta = 0.08, 95% CI [-0.66, 0.82])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.19, S.E. = 0.28, p = 0.503, 95% CI [-0.37, 0.75]; Std. beta = 0.12, 95% CI [-0.23, 0.46])
• The effect of attendance is statistically non-significant and negative (beta = -0.43, S.E. = 0.27, p = 0.115, 95% CI [-0.97, 0.10]; Std. beta = -0.30, 95% CI [-0.67, 0.07])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.46, S.E. = 0.44, p = 0.300, 95% CI [-0.41, 1.33]; Std. beta = 0.32, 95% CI [-0.28, 0.92])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: pss_pa ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.54) and the part related to the fixed effects alone (marginal R2) is of 0.15. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 46.27 (S.E. = 4.67, p < .001, 95% CI [37.13, 55.42]). Within this model:

• The effect of age is statistically non-significant and positive (beta = 0.03, S.E. = 0.06, p = 0.599, 95% CI [-0.09, 0.15]; Std. beta = 0.10, 95% CI [-0.28, 0.48])
• The effect of gender [male] is statistically non-significant and negative (beta = -3.89, S.E. = 2.02, p = 0.054, 95% CI [-7.86, 0.07]; Std. beta = -0.94, 95% CI [-1.89, 0.02])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and negative (beta = -1.11, S.E. = 1.57, p = 0.478, 95% CI [-4.19, 1.96]; Std. beta = -0.27, 95% CI [-1.01, 0.47])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.29, S.E. = 0.79, p = 0.709, 95% CI [-1.25, 1.84]; Std. beta = 0.06, 95% CI [-0.27, 0.39])
• The effect of attendance is statistically non-significant and negative (beta = -0.35, S.E. = 0.80, p = 0.664, 95% CI [-1.91, 1.22]; Std. beta = -0.08, 95% CI [-0.46, 0.29])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.18, S.E. = 1.22, p = 0.885, 95% CI [-2.22, 2.57]; Std. beta = 0.04, 95% CI [-0.53, 0.62])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: pss_ps ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.81) and the part related to the fixed effects alone (marginal R2) is of 0.16. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 17.00 (S.E. = 8.02, p = 0.034, 95% CI [1.28, 32.72]). Within this model:

• The effect of age is statistically non-significant and positive (beta = 5.05e-03, S.E. = 0.10, p = 0.961, 95% CI [-0.20, 0.21]; Std. beta = 0.01, 95% CI [-0.41, 0.43])
• The effect of gender [male] is statistically non-significant and positive (beta = 2.01, S.E. = 3.49, p = 0.565, 95% CI [-4.83, 8.86]; Std. beta = 0.31, 95% CI [-0.75, 1.37])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 5.20, S.E. = 2.68, p = 0.052, 95% CI [-0.05, 10.46]; Std. beta = 0.81, 95% CI [-8.51e-03, 1.62])
• The effect of number volunteer r is statistically non-significant and negative (beta = -1.40, S.E. = 0.88, p = 0.110, 95% CI [-3.12, 0.32]; Std. beta = -0.19, 95% CI [-0.43, 0.04])
• The effect of attendance is statistically non-significant and positive (beta = 1.08, S.E. = 1.38, p = 0.436, 95% CI [-1.63, 3.78]; Std. beta = 0.17, 95% CI [-0.25, 0.58])
• The effect of time point [2nd] is statistically significant and negative (beta = -3.33, S.E. = 1.30, p = 0.010, 95% CI [-5.88, -0.79]; Std. beta = -0.52, 95% CI [-0.91, -0.12])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: pss ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.76) and the part related to the fixed effects alone (marginal R2) is of 0.15. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 33.72 (S.E. = 12.03, p = 0.005, 95% CI [10.15, 57.29]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -0.03, S.E. = 0.16, p = 0.865, 95% CI [-0.33, 0.28]; Std. beta = -0.04, 95% CI [-0.45, 0.38])
• The effect of gender [male] is statistically non-significant and positive (beta = 5.90, S.E. = 5.23, p = 0.260, 95% CI [-4.36, 16.16]; Std. beta = 0.60, 95% CI [-0.45, 1.66])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 6.31, S.E. = 4.03, p = 0.117, 95% CI [-1.58, 14.20]; Std. beta = 0.65, 95% CI [-0.16, 1.45])
• The effect of number volunteer r is statistically non-significant and negative (beta = -1.69, S.E. = 1.45, p = 0.246, 95% CI [-4.53, 1.16]; Std. beta = -0.15, 95% CI [-0.41, 0.11])
• The effect of attendance is statistically non-significant and positive (beta = 1.42, S.E. = 2.07, p = 0.492, 95% CI [-2.63, 5.47]; Std. beta = 0.15, 95% CI [-0.27, 0.56])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -3.50, S.E. = 2.16, p = 0.106, 95% CI [-7.74, 0.74]; Std. beta = -0.36, 95% CI [-0.79, 0.08])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: rki_responsible ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.41) and the part related to the fixed effects alone (marginal R2) is of 0.26. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 17.06 (S.E. = 2.83, p < .001, 95% CI [11.52, 22.61]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -0.05, S.E. = 0.04, p = 0.137, 95% CI [-0.12, 0.02]; Std. beta = -0.24, 95% CI [-0.55, 0.08])
• The effect of gender [male] is statistically non-significant and negative (beta = -2.39, S.E. = 1.22, p = 0.050, 95% CI [-4.79, 4.40e-03]; Std. beta = -0.79, 95% CI [-1.59, 1.46e-03])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 0.91, S.E. = 0.95, p = 0.338, 95% CI [-0.96, 2.78]; Std. beta = 0.30, 95% CI [-0.32, 0.92])
• The effect of number volunteer r is statistically non-significant and negative (beta = -0.38, S.E. = 0.58, p = 0.516, 95% CI [-1.52, 0.76]; Std. beta = -0.11, 95% CI [-0.45, 0.22])
• The effect of attendance is statistically significant and positive (beta = 1.39, S.E. = 0.48, p = 0.004, 95% CI [0.45, 2.34]; Std. beta = 0.46, 95% CI [0.15, 0.77])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.48, S.E. = 0.94, p = 0.610, 95% CI [-2.33, 1.37]; Std. beta = -0.16, 95% CI [-0.77, 0.45])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: rki_nonlinear ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.51) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 12.20 (S.E. = 3.29, p < .001, 95% CI [5.75, 18.66]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -0.05, S.E. = 0.04, p = 0.273, 95% CI [-0.13, 0.04]; Std. beta = -0.22, 95% CI [-0.61, 0.17])
• The effect of gender [male] is statistically non-significant and negative (beta = -0.77, S.E. = 1.43, p = 0.591, 95% CI [-3.57, 2.03]; Std. beta = -0.27, 95% CI [-1.26, 0.72])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and negative (beta = -0.32, S.E. = 1.11, p = 0.775, 95% CI [-2.49, 1.85]; Std. beta = -0.11, 95% CI [-0.88, 0.66])
• The effect of number volunteer r is statistically non-significant and negative (beta = -0.31, S.E. = 0.55, p = 0.582, 95% CI [-1.39, 0.78]; Std. beta = -0.10, 95% CI [-0.44, 0.25])
• The effect of attendance is statistically non-significant and positive (beta = 0.71, S.E. = 0.56, p = 0.207, 95% CI [-0.39, 1.81]; Std. beta = 0.25, 95% CI [-0.14, 0.64])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, S.E. = 0.86, p = 0.973, 95% CI [-1.65, 1.71]; Std. beta = 0.01, 95% CI [-0.58, 0.61])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: rki_peer ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.49) and the part related to the fixed effects alone (marginal R2) is of 0.07. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 21.61 (S.E. = 2.61, p < .001, 95% CI [16.50, 26.73]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -0.01, S.E. = 0.03, p = 0.720, 95% CI [-0.08, 0.05]; Std. beta = -0.07, 95% CI [-0.47, 0.32])
• The effect of gender [male] is statistically non-significant and negative (beta = -0.78, S.E. = 1.13, p = 0.492, 95% CI [-3.00, 1.44]; Std. beta = -0.35, 95% CI [-1.35, 0.65])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 0.12, S.E. = 0.88, p = 0.892, 95% CI [-1.60, 1.84]; Std. beta = 0.05, 95% CI [-0.72, 0.83])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.43, S.E. = 0.44, p = 0.328, 95% CI [-0.44, 1.30]; Std. beta = 0.17, 95% CI [-0.17, 0.52])
• The effect of attendance is statistically non-significant and negative (beta = -0.32, S.E. = 0.45, p = 0.467, 95% CI [-1.20, 0.55]; Std. beta = -0.15, 95% CI [-0.54, 0.25])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 1.11, S.E. = 0.69, p = 0.108, 95% CI [-0.24, 2.45]; Std. beta = 0.50, 95% CI [-0.11, 1.10])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: rki_expect ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.38) and the part related to the fixed effects alone (marginal R2) is of 0.14. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 5.29 (S.E. = 1.19, p < .001, 95% CI [2.95, 7.63]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -0.03, S.E. = 0.02, p = 0.095, 95% CI [-0.06, 4.44e-03]; Std. beta = -0.30, 95% CI [-0.66, 0.05])
• The effect of gender [male] is statistically non-significant and positive (beta = 0.11, S.E. = 0.52, p = 0.838, 95% CI [-0.91, 1.12]; Std. beta = 0.09, 95% CI [-0.81, 0.99])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and negative (beta = -0.51, S.E. = 0.40, p = 0.206, 95% CI [-1.30, 0.28]; Std. beta = -0.45, 95% CI [-1.15, 0.25])
• The effect of number volunteer r is statistically non-significant and negative (beta = -0.03, S.E. = 0.23, p = 0.887, 95% CI [-0.49, 0.42]; Std. beta = -0.03, 95% CI [-0.39, 0.33])
• The effect of attendance is statistically non-significant and positive (beta = 0.18, S.E. = 0.20, p = 0.367, 95% CI [-0.21, 0.58]; Std. beta = 0.16, 95% CI [-0.19, 0.52])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.27, S.E. = 0.37, p = 0.467, 95% CI [-0.46, 1.00]; Std. beta = 0.24, 95% CI [-0.41, 0.89])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: rki ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.50) and the part related to the fixed effects alone (marginal R2) is of 0.29. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 56.19 (S.E. = 4.44, p < .001, 95% CI [47.49, 64.89]). Within this model:

• The effect of age is statistically significant and negative (beta = -0.14, S.E. = 0.06, p = 0.015, 95% CI [-0.25, -0.03]; Std. beta = -0.39, 95% CI [-0.71, -0.08])
• The effect of gender [male] is statistically significant and negative (beta = -3.82, S.E. = 1.92, p = 0.047, 95% CI [-7.59, -0.06]; Std. beta = -0.82, 95% CI [-1.63, -0.01])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 0.22, S.E. = 1.49, p = 0.882, 95% CI [-2.71, 3.15]; Std. beta = 0.05, 95% CI [-0.58, 0.68])
• The effect of number volunteer r is statistically non-significant and negative (beta = -0.32, S.E. = 0.86, p = 0.710, 95% CI [-2.00, 1.36]; Std. beta = -0.06, 95% CI [-0.38, 0.26])
• The effect of attendance is statistically significant and positive (beta = 1.96, S.E. = 0.76, p = 0.009, 95% CI [0.48, 3.44]; Std. beta = 0.42, 95% CI [0.10, 0.74])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.88, S.E. = 1.37, p = 0.518, 95% CI [-1.80, 3.56]; Std. beta = 0.19, 95% CI [-0.39, 0.77])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: raq_possible ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.47) and the part related to the fixed effects alone (marginal R2) is of 0.10. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 14.63 (S.E. = 1.85, p < .001, 95% CI [11.00, 18.26]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -8.75e-03, S.E. = 0.02, p = 0.713, 95% CI [-0.06, 0.04]; Std. beta = -0.07, 95% CI [-0.45, 0.31])
• The effect of gender [male] is statistically non-significant and negative (beta = -1.08, S.E. = 0.80, p = 0.178, 95% CI [-2.65, 0.49]; Std. beta = -0.66, 95% CI [-1.63, 0.30])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 0.84, S.E. = 0.62, p = 0.175, 95% CI [-0.38, 2.06]; Std. beta = 0.52, 95% CI [-0.23, 1.27])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.05, S.E. = 0.33, p = 0.872, 95% CI [-0.59, 0.69]; Std. beta = 0.03, 95% CI [-0.32, 0.38])
• The effect of attendance is statistically non-significant and positive (beta = 0.17, S.E. = 0.32, p = 0.601, 95% CI [-0.45, 0.79]; Std. beta = 0.10, 95% CI [-0.28, 0.48])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.27, S.E. = 0.51, p = 0.593, 95% CI [-0.73, 1.27]; Std. beta = 0.17, 95% CI [-0.45, 0.78])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: raq_difficulty ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.52) and the part related to the fixed effects alone (marginal R2) is of 0.07. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 10.59 (S.E. = 1.77, p < .001, 95% CI [7.12, 14.05]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -6.91e-03, S.E. = 0.02, p = 0.762, 95% CI [-0.05, 0.04]; Std. beta = -0.06, 95% CI [-0.47, 0.34])
• The effect of gender [male] is statistically non-significant and negative (beta = -0.50, S.E. = 0.77, p = 0.516, 95% CI [-2.00, 1.01]; Std. beta = -0.34, 95% CI [-1.36, 0.68])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 0.53, S.E. = 0.59, p = 0.372, 95% CI [-0.63, 1.70]; Std. beta = 0.36, 95% CI [-0.43, 1.15])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.11, S.E. = 0.29, p = 0.714, 95% CI [-0.46, 0.67]; Std. beta = 0.06, 95% CI [-0.28, 0.41])
• The effect of attendance is statistically non-significant and positive (beta = 0.31, S.E. = 0.30, p = 0.303, 95% CI [-0.28, 0.91]; Std. beta = 0.21, 95% CI [-0.19, 0.61])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.24, S.E. = 0.45, p = 0.589, 95% CI [-0.63, 1.12]; Std. beta = 0.16, 95% CI [-0.43, 0.76])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: raq ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.54) and the part related to the fixed effects alone (marginal R2) is of 0.08. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 25.24 (S.E. = 3.53, p < .001, 95% CI [18.33, 32.15]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -0.02, S.E. = 0.05, p = 0.732, 95% CI [-0.10, 0.07]; Std. beta = -0.07, 95% CI [-0.47, 0.33])
• The effect of gender [male] is statistically non-significant and negative (beta = -1.57, S.E. = 1.53, p = 0.304, 95% CI [-4.57, 1.43]; Std. beta = -0.53, 95% CI [-1.54, 0.48])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 1.39, S.E. = 1.18, p = 0.242, 95% CI [-0.94, 3.71]; Std. beta = 0.47, 95% CI [-0.32, 1.25])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.13, S.E. = 0.57, p = 0.821, 95% CI [-0.99, 1.25]; Std. beta = 0.04, 95% CI [-0.30, 0.37])
• The effect of attendance is statistically non-significant and positive (beta = 0.48, S.E. = 0.60, p = 0.428, 95% CI [-0.71, 1.66]; Std. beta = 0.16, 95% CI [-0.24, 0.56])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.48, S.E. = 0.88, p = 0.582, 95% CI [-1.24, 2.20]; Std. beta = 0.16, 95% CI [-0.42, 0.74])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: who ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.72) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 17.41 (S.E. = 5.80, p = 0.003, 95% CI [6.05, 28.78]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -0.07, S.E. = 0.07, p = 0.331, 95% CI [-0.22, 0.07]; Std. beta = -0.22, 95% CI [-0.65, 0.22])
• The effect of gender [male] is statistically non-significant and negative (beta = -1.11, S.E. = 2.52, p = 0.661, 95% CI [-6.05, 3.84]; Std. beta = -0.25, 95% CI [-1.34, 0.85])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and negative (beta = -0.85, S.E. = 1.94, p = 0.661, 95% CI [-4.66, 2.95]; Std. beta = -0.19, 95% CI [-1.03, 0.66])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.43, S.E. = 0.71, p = 0.546, 95% CI [-0.97, 1.83]; Std. beta = 0.09, 95% CI [-0.19, 0.36])
• The effect of attendance is statistically non-significant and positive (beta = 0.16, S.E. = 1.00, p = 0.872, 95% CI [-1.79, 2.11]; Std. beta = 0.04, 95% CI [-0.40, 0.47])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.62, S.E. = 1.07, p = 0.558, 95% CI [-1.47, 2.72]; Std. beta = 0.14, 95% CI [-0.33, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: phq ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.85) and the part related to the fixed effects alone (marginal R2) is of 0.08. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at -2.57 (S.E. = 4.96, p = 0.604, 95% CI [-12.28, 7.14]). Within this model:

• The effect of age is statistically non-significant and positive (beta = 0.02, S.E. = 0.06, p = 0.711, 95% CI [-0.10, 0.15]; Std. beta = 0.08, 95% CI [-0.36, 0.53])
• The effect of gender [male] is statistically non-significant and positive (beta = 0.55, S.E. = 2.16, p = 0.798, 95% CI [-3.68, 4.79]; Std. beta = 0.15, 95% CI [-0.97, 1.26])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 1.51, S.E. = 1.66, p = 0.362, 95% CI [-1.74, 4.76]; Std. beta = 0.40, 95% CI [-0.46, 1.25])
• The effect of number volunteer r is statistically non-significant and negative (beta = -0.47, S.E. = 0.46, p = 0.307, 95% CI [-1.36, 0.43]; Std. beta = -0.11, 95% CI [-0.32, 0.10])
• The effect of attendance is statistically non-significant and positive (beta = 0.89, S.E. = 0.85, p = 0.299, 95% CI [-0.79, 2.56]; Std. beta = 0.23, 95% CI [-0.21, 0.67])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.36, S.E. = 0.67, p = 0.596, 95% CI [-1.67, 0.96]; Std. beta = -0.09, 95% CI [-0.44, 0.25])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: gad ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.83) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 0.69 (S.E. = 4.11, p = 0.866, 95% CI [-7.35, 8.74]). Within this model:

• The effect of age is statistically non-significant and negative (beta = -1.97e-03, S.E. = 0.05, p = 0.970, 95% CI [-0.11, 0.10]; Std. beta = -8.31e-03, 95% CI [-0.45, 0.43])
• The effect of gender [male] is statistically non-significant and positive (beta = 2.37, S.E. = 1.79, p = 0.185, 95% CI [-1.13, 5.88]; Std. beta = 0.75, 95% CI [-0.36, 1.86])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 0.64, S.E. = 1.37, p = 0.643, 95% CI [-2.05, 3.33]; Std. beta = 0.20, 95% CI [-0.65, 1.05])
• The effect of number volunteer r is statistically non-significant and negative (beta = -0.21, S.E. = 0.40, p = 0.592, 95% CI [-0.99, 0.57]; Std. beta = -0.06, 95% CI [-0.28, 0.16])
• The effect of attendance is statistically non-significant and positive (beta = 0.37, S.E. = 0.71, p = 0.604, 95% CI [-1.02, 1.75]; Std. beta = 0.12, 95% CI [-0.32, 0.55])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.21, S.E. = 0.59, p = 0.717, 95% CI [-0.94, 1.36]; Std. beta = 0.07, 95% CI [-0.30, 0.43])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: nb_pcs ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.43. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 65.47 (S.E. = 5.62, p < .001, 95% CI [54.46, 76.48]). Within this model:

• The effect of age is statistically significant and negative (beta = -0.29, S.E. = 0.07, p < .001, 95% CI [-0.43, -0.15]; Std. beta = -0.56, 95% CI [-0.83, -0.29])
• The effect of gender [male] is statistically non-significant and negative (beta = -1.38, S.E. = 2.43, p = 0.570, 95% CI [-6.13, 3.38]; Std. beta = -0.20, 95% CI [-0.89, 0.49])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and positive (beta = 0.47, S.E. = 1.89, p = 0.805, 95% CI [-3.24, 4.17]; Std. beta = 0.07, 95% CI [-0.47, 0.60])
• The effect of number volunteer r is statistically non-significant and positive (beta = 2.11, S.E. = 1.14, p = 0.063, 95% CI [-0.12, 4.34]; Std. beta = 0.27, 95% CI [-0.02, 0.56])
• The effect of attendance is statistically non-significant and negative (beta = -1.13, S.E. = 0.95, p = 0.236, 95% CI [-3.00, 0.74]; Std. beta = -0.16, 95% CI [-0.43, 0.11])
• The effect of time point [2nd] is statistically significant and positive (beta = 3.98, S.E. = 1.84, p = 0.030, 95% CI [0.38, 7.58]; Std. beta = 0.58, 95% CI [0.05, 1.10])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with age, gender, previous_volunteer, number_volunteer_r, attendance and time_point (formula: nb_mcs ~ 1 + age + gender + previous_volunteer + number_volunteer_r + attendance + time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to age = 0, gender = female, previous_volunteer = yes_beyong_12_month, number_volunteer_r = 0, attendance = 0 and time_point = 1st , is at 53.24 (S.E. = 8.81, p < .001, 95% CI [35.98, 70.50]). Within this model:

• The effect of age is statistically non-significant and positive (beta = 0.04, S.E. = 0.11, p = 0.748, 95% CI [-0.19, 0.26]; Std. beta = 0.07, 95% CI [-0.36, 0.49])
• The effect of gender [male] is statistically non-significant and negative (beta = -2.08, S.E. = 3.82, p = 0.586, 95% CI [-9.58, 5.41]; Std. beta = -0.30, 95% CI [-1.37, 0.78])
• The effect of previous volunteer [yes_within_12_month] is statistically non-significant and negative (beta = -0.97, S.E. = 2.95, p = 0.742, 95% CI [-6.76, 4.82]; Std. beta = -0.14, 95% CI [-0.97, 0.69])
• The effect of number volunteer r is statistically non-significant and positive (beta = 0.18, S.E. = 1.33, p = 0.892, 95% CI [-2.42, 2.78]; Std. beta = 0.02, 95% CI [-0.31, 0.35])
• The effect of attendance is statistically non-significant and negative (beta = -0.47, S.E. = 1.51, p = 0.755, 95% CI [-3.43, 2.49]; Std. beta = -0.07, 95% CI [-0.49, 0.36])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.30, S.E. = 2.02, p = 0.884, 95% CI [-4.25, 3.66]; Std. beta = -0.04, 95% CI [-0.61, 0.52])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.